1. What It Is To Be Conscious: Exploring the Plausibility of Consciousness in Deep Learning Computers (Peter) Zach Davis Philosophy & Computer Science ID Advisors: Kristina Striegnitz and David Barnett

2. Motivation Deep learning computers are amazing!

3. Motivation Deep learning computers are amazing! But… No consensus on their consciousness

4. Machine Learning  Derive generalizations from examples

5. Machine Learning  Derive generalizations from examples  Similar to humans  Derive generalizations from examples  Similar to humans

6. Machine Learning  Derive generalizations from examples  Similar to humans  One method uses artificial neural networks  Derive generalizations from examples  Similar to humans  One method uses artificial neural networks

7. Artificial Neural Networks Single Perceptron General model for neuron Single Perceptron General model for neuron

8. Artificial Neural Networks Single Perceptron General model for neuron Used in: 1.Feed-Forward Neural Networks 2.Recurrent Neural Networks Single Perceptron General model for neuron Used in: 1.Feed-Forward Neural Networks 2.Recurrent Neural Networks

9. Artificial Neural Networks Feed-Forward Networks o Most common type o Neural links only go forward o Like an assembly line o Output becomes input for next layer Feed-Forward Networks o Most common type o Neural links only go forward o Like an assembly line o Output becomes input for next layer

10. Artificial Neural Networks Recurrent Networks o More complex o Neural links are bidirectional o Output can be input for: o Next layer o Current layer o Previous layer o Support memory Recurrent Networks o More complex o Neural links are bidirectional o Output can be input for: o Next layer o Current layer o Previous layer o Support memory

11. Deep Learning – Type of machine learning – Specific structure: Deep (lots of) layers of neural networks – Examples: Convolutional Neural Networks Deep Belief Networks – Type of machine learning – Specific structure: Deep (lots of) layers of neural networks – Examples: Convolutional Neural Networks Deep Belief Networks

12. Deep Learning Convolutional Neural Networks  Feed-forward network  Neurons correspond to overlapping parts of the image  Outputs from layers are pooled Convolutional Neural Networks  Feed-forward network  Neurons correspond to overlapping parts of the image  Outputs from layers are pooled

13. Deep Learning Deep Belief Networks  Layers learn in top-down approach  Layers depend on other layers  Can reconstruct inputs  Generative model  e.g. generate an image Deep Belief Networks  Layers learn in top-down approach  Layers depend on other layers  Can reconstruct inputs  Generative model  e.g. generate an image

14. But are they conscious??

15. Consciousness (Functionalism) Multiple Drafts Model Daniel Dennett  Brain activity is parallel  Information is continually revisable and accessible

16. Consciousness (Functionalism) Multiple Drafts Model Daniel Dennett  Brain activity is parallel  Information is continually revisable and accessible  ‘Qualia’ don’t really exist

17. Consciousness (Functionalism) Multiple Drafts Model Daniel Dennett  Brain activity is parallel  Information is continually revisable and accessible  ‘Qualia’ don’t really exist  Consciousness = the functional effects of judgments

18. Are Deep Learning Computers Conscious? Multiple Drafts Model – Consciousness doesn’t “need” qualia

19. Are Deep Learning Computers Conscious? Multiple Drafts Model – Consciousness doesn’t “need” qualia – Deep Learning computers: Function consciously Process information consciously

20. Are Deep Learning Computers Conscious? Multiple Drafts Model – Consciousness doesn’t “need” qualia – Deep Learning computers: Function consciously Process information consciously – Thus: computers are conscious

21. Consciousness (Partial Physicalism) Hybrid Theory Ned Block  Physicalism: Conscious states = Physical states

22. Consciousness (Partial Physicalism) Hybrid Theory Ned Block  Physicalism: Conscious states = Physical states o Access-consciousness (A-consciousness) states that are available for rational processes o Phenomenal-consciousness (P-consciousness) “what it is like-ness”

23. Consciousness (Partial Physicalism) Hybrid Theory Ned Block  Physicalism: Conscious states = Physical states o Access-consciousness (A-consciousness) states that are available for rational processes o Phenomenal-consciousness (P-consciousness) “what it is like-ness” – ‘Consciousness’ refers to A- and P-states – Physical make-up matters!

24. Are Deep Learning Computers Conscious? Hybrid Theory – Consciousness -> both A-states and P-states

25. Are Deep Learning Computers Conscious? Hybrid Theory – Consciousness -> both A-states and P-states – Deep learning computers aren’t P-conscious They don’t support P-consciousness

26. Are Deep Learning Computers Conscious? Hybrid Theory – Consciousness -> both A-states and P-states – Deep learning computers aren’t P-conscious They don’t support P-consciousness – Thus: computers are unconscious But they are A-conscious

27. Consciousness (Modified Functionalism) Integrated Information Theory Giulio Tononi Consciousness depends on:

28. Consciousness (Modified Functionalism) Integrated Information Theory Giulio Tononi Consciousness depends on: – Information: number of possible alternative outcomes (based on entropy) – Integration: interdependency between parts of the system

29. Consciousness (Modified Functionalism) Integrated Information Theory Giulio Tononi Consciousness depends on: – Information: number of possible alternative outcomes (based on entropy) – Integration: interdependency between parts of the system Amount of consciousness relates to 1.Amount of information in the system 2.Degree of interdependency in subsystems

30. Are Deep Learning Computers Conscious? Integrated Information Theory – Consciousness = information integration

31. Are Deep Learning Computers Conscious? Integrated Information Theory – Consciousness = information integration – Feed-back is important

32. Are Deep Learning Computers Conscious? Integrated Information Theory – Consciousness = information integration – Feed-back is important – Thus: Feed-forward networks (convolutional networks)  not conscious Recurrent networks (deep belief networks)  are conscious *Consciousness varies with design

33. Where Do We Go From Here? Which theory is correct?

34. Where Do We Go From Here? Which theory is correct? How do we find out? – Philosophical debate – Empirical research » Consciousness science » Neural Network Design